Table of Contents

  • 1  Second Level Design Matrix
  • 2  Valence x Outcome Sanity Check
  • 3  Adolescent Cohort
    • 3.1  Monetary Task
      • 3.1.1  Positive win > Positive loss
      • 3.1.2  All wins > All losses
    • 3.2  Social Task
      • 3.2.1  Positive win > Positive loss
      • 3.2.2  All wins > All losses
  • 4  Young Adult Cohort
    • 4.1  Monetary Task
      • 4.1.1  Positive win > Positive loss
      • 4.1.2  All wins > All losses
    • 4.2  Social Task
      • 4.2.1  Positive win > Positive loss
      • 4.2.2  All wins > All losses
  • 5  Group differences
    • 5.1  Monetary Task
    • 5.2  Social Task
      • 5.2.1  Positive win > Positive loss
      • 5.2.2  All wins > All losses
  • 6  Accounting for Age
In [1]:
import os
import glob
import pandas as pd
import numpy as np

from matplotlib.gridspec import GridSpec

# Import cerebellum packages
import matplotlib.pyplot as plt
import SUITPy.flatmap as flatmap

from nilearn.glm.second_level import make_second_level_design_matrix
from nilearn.plotting import plot_design_matrix, plot_stat_map
from nilearn.glm.second_level import SecondLevelModel
from nilearn.glm import threshold_stats_img

import importlib

import helpful_functions
importlib.reload(helpful_functions)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/__init__.py:67: FutureWarning: Python 3.7 support is deprecated and will be removed in release 0.12 of Nilearn. Consider switching to Python 3.9 or 3.10.
  _python_deprecation_warnings()
Out[1]:
<module 'helpful_functions' from '/Users/haroonpopal/Library/CloudStorage/OneDrive-TempleUniversity/olson_lab/projects/social_doors/code/helpful_functions.py'>
In [2]:
#bids_dir = '/data/projects/social_doors/'
bids_dir = '/Users/haroonpopal/OneDrive - Temple University/olson_lab/projects/social_doors'


os.chdir(bids_dir)

data_dir = os.path.join(bids_dir, 'derivatives','social_doors-nilearn')
outp_dir = os.path.join(bids_dir, 'derivatives', 'social_doors-nilearn')


# Define subject list
#subjs_scan_info = pd.read_csv(bids_dir+'/derivatives/mriqc/mriqc_summary_poor.csv')
#subjs_list = list(subjs_scan_info['subject'].unique())
#subjs_list.sort()
subjs_info = pd.read_csv(bids_dir+'/derivatives/participants_good.tsv', sep='\t', index_col=0)
subjs_info = subjs_info.rename(columns={'participant_id': 'subject_label'})

# Remove participants with bad data
subjs_info = subjs_info[subjs_info['subject_label'].str.contains('sub-3880')==False]
subjs_info = subjs_info[subjs_info['subject_label'].str.contains('sub-4069')==False]


subjs_info_kids = subjs_info[subjs_info['group']=='kid']
subjs_info_colg = subjs_info[subjs_info['group']=='college']

subjs_list = subjs_info['subject_label'].to_list()
subjs_list_kids = subjs_info_kids['subject_label'].to_list()
subjs_list_colg = subjs_info_colg['subject_label'].to_list()

print('Found '+str(len(subjs_list_kids))+' adolescent subjects')
print('Found '+str(len(subjs_list_colg))+' college subjects')

Found 32 adolescent subjects
Found 29 college subjects

Adolescents¶

In [3]:
# Filter for releveant data
subjs_info_fltr = subjs_info[['subject_label', 'age']]
subjs_info_kids_fltr = subjs_info_kids[['subject_label','age']]
subjs_info_colg_fltr = subjs_info_colg[['subject_label','age']]
In [6]:
design_matrix = make_second_level_design_matrix(subjs_list_kids, subjs_info_kids_fltr)

plot_design_matrix(design_matrix)
Out[6]:
<AxesSubplot:label='conditions', ylabel='scan number'>
In [14]:
design_matrix = make_second_level_design_matrix(subjs_list_kids, subjs_info_kids_fltr)
mni_mask = bids_dir+"/derivatives/fmriprep/sub-010/anat/sub-010_space-MNI152NLin2009cAsym_label-GM_probseg_bin.nii.gz"

#contrasts = ['valence_x_outcome',
#             'positive','positive_win','positive_loss',
#             'negative','negative_win','negative_loss']
contrasts=['all_winVlos', 'positive_winVlos']
contrasts_2nd = ['age','intercept']
tasks = ['mdoors','social']
group = 'adole'
mc = 'fdr'
alpha = 0.05


threshold_data = {}

for task in tasks:
    for contrast in contrasts:
        
        temp_file_list = []
        for subj in subjs_list_kids:
            temp_file = glob.glob(os.path.join(data_dir,subj,'zmap_'+task+'_'+contrast+'.nii'))
            temp_file_list.append(temp_file[0])
        temp_file_list.sort()
        
        print('Calculating '+task+' '+group+' '+contrast+' contrast')
        
        # Fit the second level GLM
        model = SecondLevelModel(mask_img=mni_mask, smoothing_fwhm=8.0)
        model.fit(temp_file_list, design_matrix=design_matrix)
        
        for contrast_2nd in contrasts_2nd:
            # Compute a specific contrast (e.g. pos reward vs neg reward)
            contrast_stats = model.compute_contrast(contrast_2nd, output_type='all')
            
            # Save the z-score stat map and the effect size map
            z_map = contrast_stats['z_score']
            e_map = contrast_stats['effect_size']

            z_map.to_filename(os.path.join(data_dir,'group',
                                       group+'_'+task+'_'+contrast+'_'+contrast_2nd+'_zmap.nii.gz'))
            e_map.to_filename(os.path.join(data_dir,'group',
                                       group+'_'+task+'_'+contrast+'_'+contrast_2nd+'_effect.nii.gz'))

            
            # Multiple Comparisons Correction
            z_map_thresh, threshold = threshold_stats_img(z_map, alpha=alpha, height_control=mc)
            z_map_thresh.to_filename(os.path.join(data_dir,'group',
                                       group+'_'+task+'_'+contrast+'_'+contrast_2nd+'_zmap_'+mc+'-'+str(alpha)+'.nii.gz'))
            
            threshold_data['zmap_'+group+'_'+task+'_'+contrast+'_'+contrast_2nd+'_zmap_'+mc+'-'+str(alpha)] = threshold
            

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/glm/first_level/design_matrix.py:512: UserWarning: Attention: Design matrix is singular. Aberrant estimates are expected.
  "Attention: Design matrix is singular. Aberrant estimates "

Calculating mdoors adole all_winVlos contrast
Calculating mdoors adole positive_winVlos contrast

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 3.9708165692795103. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 3.958485445639618. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "

Calculating social adole all_winVlos contrast

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 4.510752872318276. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 4.508400631846887. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "

Calculating social adole positive_winVlos contrast

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 3.4942643837543077. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 3.5963192432734217. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "

Monetary Task¶

Positive win > Positive loss¶

In [15]:
filename = data_dir+'/group/'+'adole_mdoors_positive_winVlos_intercept'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs

All wins > All losses¶

In [16]:
filename = data_dir+'/group/'+'adole_mdoors_all_winVlos_intercept'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs

Social Task¶

Positive win > Positive loss¶

In [17]:
filename = data_dir+'/group/'+'adole_social_positive_winVlos_intercept'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs

All wins > All losses¶

In [18]:
filename = data_dir+'/group/'+'adole_social_all_winVlos_intercept'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs

Young Adult Cohort¶

In [19]:
design_matrix = make_second_level_design_matrix(subjs_list_colg, subjs_info_colg_fltr)


contrasts=['all_winVlos', 'positive_winVlos']
contrasts_2nd = ['age','intercept']
tasks = ['mdoors','social']
group = 'adult'
mc = 'fdr'
alpha = 0.05


threshold_data = {}

for task in tasks:
    for contrast in contrasts:
        
        temp_file_list = []
        for subj in subjs_list_colg:
            temp_file = glob.glob(os.path.join(data_dir,subj,'zmap_'+task+'_'+contrast+'.nii'))
            temp_file_list.append(temp_file[0])
        temp_file_list.sort()
        
        print('Calculating '+task+' group '+contrast+' contrast')
        
        model = SecondLevelModel(mask_img=mni_mask, smoothing_fwhm=8.0)
        model.fit(temp_file_list, design_matrix=design_matrix)
        
        for contrast_2nd in contrasts_2nd:
            # Compute a specific contrast (e.g. pos reward vs neg reward)
            contrast_stats = model.compute_contrast(contrast_2nd, output_type='all')
            
            # Save the z-score stat map and the effect size map
            z_map = contrast_stats['z_score']
            e_map = contrast_stats['effect_size']

            z_map.to_filename(os.path.join(data_dir,'group',
                                       group+'_'+task+'_'+contrast+'_'+contrast_2nd+'_zmap.nii.gz'))
            e_map.to_filename(os.path.join(data_dir,'group',
                                       group+'_'+task+'_'+contrast+'_'+contrast_2nd+'_effect.nii.gz'))

            
            # Multiple Comparisons Correction
            z_map_thresh, threshold = threshold_stats_img(z_map, alpha=alpha, height_control=mc)
            z_map_thresh.to_filename(os.path.join(data_dir,'group',
                                       group+'_'+task+'_'+contrast+'_'+contrast_2nd+'_zmap_'+mc+'-'+str(alpha)+'.nii.gz'))
            
            threshold_data['zmap_'+group+'_'+task+'_'+contrast+'_'+contrast_2nd+'_zmap_'+mc+'-'+str(alpha)] = threshold
            

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/glm/first_level/design_matrix.py:512: UserWarning: Attention: Design matrix is singular. Aberrant estimates are expected.
  "Attention: Design matrix is singular. Aberrant estimates "

Calculating mdoors group all_winVlos contrast

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 4.203566601487279. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 4.167974465346996. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "

Calculating mdoors group positive_winVlos contrast

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 3.9141796562960023. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 4.084015253903517. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "

Calculating social group all_winVlos contrast

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 4.3268064045150165. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 3.9574459271213835. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "

Calculating social group positive_winVlos contrast

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 4.241392440352204. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 3.8821727666010686. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "

Monetary Task¶

Positive win > Positive loss¶

In [20]:
filename = data_dir+'/group/'+'adult_mdoors_positive_winVlos_intercept'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs

All wins > All losses¶

In [21]:
filename = data_dir+'/group/'+'adult_mdoors_all_winVlos_intercept'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs

Social Task¶

Positive win > Positive loss¶

In [23]:
filename = data_dir+'/group/'+'adult_social_positive_winVlos_intercept'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs

All wins > All losses¶

In [24]:
filename = data_dir+'/group/'+'adult_social_all_winVlos_intercept'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs

All Subjects¶

In [25]:
subjs_info_num = subjs_info.copy()
subjs_info_num = subjs_info_num.replace({'sex': {'F': 0, 'M': 1},
                                         'group': {'college': -1, 'kid': 1}})
subjs_info_num = subjs_info_num.drop(columns=['age', 'sex'])

design_matrix = make_second_level_design_matrix(subjs_list, subjs_info_num)

plot_design_matrix(design_matrix)
Out[25]:
<AxesSubplot:label='conditions', ylabel='scan number'>
In [26]:
#design_matrix = make_second_level_design_matrix(subjs_list, subjs_info_fltr)

#contrasts = ['valence_x_outcome',
#             'positive','positive_win','positive_loss',
#             'negative','negative_win','negative_loss']
contrasts=['all_winVlos', 'positive_winVlos']
contrasts_2nd = ['intercept','group']
tasks = ['mdoors','social']
group = 'all'
mc = 'fdr'
alpha = 0.05

threshold_all = {}

#temp_file_list = []
#for subj in subjs_list_kids:
#    temp_file = glob.glob(os.path.join(data_dir,subj,'zmap_'+task+'_'+contrast+'.nii.gz'))
#    temp_file_list.append(temp_file[0])
for task in tasks:
    for contrast in contrasts:
        
        temp_file_list = []
        for subj in subjs_list:
            temp_file = glob.glob(os.path.join(data_dir,subj,'zmap_'+task+'_'+contrast+'.nii'))
            temp_file_list.append(temp_file[0])
        temp_file_list.sort()
        
        print('Calculating '+task+' '+group+' '+contrast+' contrast')
        
        model = SecondLevelModel(mask_img=mni_mask, smoothing_fwhm=8.0)
        model.fit(temp_file_list, design_matrix=design_matrix)
        
        for contrast_2nd in contrasts_2nd:
            # Compute a specific contrast (e.g. pos reward vs neg reward)
            contrast_stats = model.compute_contrast(contrast_2nd, output_type='all')
            
            # Save the z-score stat map and the effect size map
            z_map = contrast_stats['z_score']
            e_map = contrast_stats['effect_size']

            z_map.to_filename(os.path.join(data_dir,'group',
                                       group+'_'+task+'_'+contrast+'_'+contrast_2nd+'_zmap.nii.gz'))
            e_map.to_filename(os.path.join(data_dir,'group',
                                       group+'_'+task+'_'+contrast+'_'+contrast_2nd+'_effect.nii.gz'))

            
            # Multiple Comparisons Correction
            z_map_thresh, threshold = threshold_stats_img(z_map, alpha=alpha, height_control=mc)
            z_map_thresh.to_filename(os.path.join(data_dir,'group',
                                       group+'_'+task+'_'+contrast+'_'+contrast_2nd+'_zmap_'+mc+'-'+str(alpha)+'.nii.gz'))
            
            threshold_data['zmap_'+group+'_'+task+'_'+contrast+'_'+contrast_2nd+'_zmap_'+mc+'-'+str(alpha)] = threshold
            

Calculating mdoors all all_winVlos contrast

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 3.4083026196757293. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "

Calculating mdoors all positive_winVlos contrast

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 3.443543850800665. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "

Calculating social all all_winVlos contrast

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/_utils/param_validation.py:73: UserWarning: The given float value must not exceed 4.381922649003004. But, you have given threshold=inf.
  f"The given float value must not exceed {value_check}. "

Calculating social all positive_winVlos contrast

Group differences¶

Where adolescents > young adults

Monetary Task¶

Positive win > Positive loss¶

In [27]:
filename = data_dir+'/group/'+'all_mdoors_positive_winVlos_group'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs

All wins > All losses¶

In [28]:
filename = data_dir+'/group/'+'all_mdoors_all_winVlos_group'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs

Social Task¶

Positive win > Positive loss¶

In [29]:
filename = data_dir+'/group/'+'all_social_positive_winVlos_group'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs

All wins > All losses¶

In [30]:
filename = data_dir+'/group/'+'all_social_all_winVlos_group'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_slicers.py:434: UserWarning: empty mask
  new_img_like(img, not_mask, affine)
/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs

Accounting for Group¶

Monetary Task¶

Positive win > Positive loss¶

In [31]:
filename = data_dir+'/group/'+'all_mdoors_positive_winVlos_intercept'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs

All wins > All losses¶

In [33]:
filename = data_dir+'/group/'+'all_mdoors_all_winVlos_intercept'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/plotting/displays/_axes.py:75: UserWarning: No contour levels were found within the data range.
  data_2d.copy(), extent=(xmin, xmax, zmin, zmax), **kwargs

Social Task¶

Positive wins > Positive losses¶

In [34]:
filename = data_dir+'/group/'+'all_social_positive_winVlos_intercept'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)

All wins > All losses¶

In [35]:
filename = data_dir+'/group/'+'all_social_all_winVlos_intercept'

helpful_functions.plot_transparent_threshold(filename, thresh=.05)
In [40]:
plot_stat_contours('zmap_all_social_all_winVlos_intercept', mc='fdr', mc_alpha=0.05, thresh=1)

Behavioral Analysis¶

Not updated

In [62]:
behav_data = pd.read_excel(bids_dir+'/derivatives/behavioral/aggregate_full_data_SPSS_020419.xlsx',
                          sheet_name='Sheet1')

behav_data.head()
Out[62]:
Order Subject ch_totanx ch_socialanx p_totanx p_socialanx bfne ch_cdi_total ch_cdi_interpersonal ch_cdi_anhedonia ... FB_VS_6mm_R_Like_Lose_NZMean_1 FB_VS_6mm_R_Like_Lose_NZCount_1 FB_VS_6mm_R_Like_Lose_NZMin_1 FB_VS_6mm_R_Like_Lose_NZMax_1 FB_VS_6mm_R_Like_Win_Mean_1 FB_VS_6mm_R_Like_Win_NZMean_1 FB_VS_6mm_R_Like_Win_NZCount_1 FB_VS_6mm_R_Like_Win_NZMin_1 FB_VS_6mm_R_Like_Win_NZMax_1 FB_VS6mm_L_Correct_DislikeVsLike
0 1 s010 51 14 7 1 33 11.0 0.0 5.0 ... 0.029036 123.0 -0.296210 0.647286 0.256033 0.256033 123.0 -0.022722 0.752379 -0.127002
1 1 s011 12 3 4 1 31 8.0 0.0 3.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 1 s013 8 4 1 1 13 5.0 0.0 2.0 ... -0.308424 123.0 -0.554069 0.030482 0.093248 0.093248 123.0 -0.183241 1.003483 -0.167305
3 1 s015 18 5 1 0 21 NaN NaN NaN ... -0.798996 123.0 -1.841710 -0.031699 0.004660 0.004660 123.0 -1.020790 0.809193 0.485397
4 2 s024 21 10 0 0 28 1.0 0.0 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 1551 columns

In [63]:
behav_data_fltr = behav_data[['Subject','ch_totanx', 'ch_cdi_total']]

behav_data_fltr['Subject'] = behav_data_fltr['Subject'].str.replace('s','sub-')

behav_data_fltr = behav_data_fltr.rename(columns={'Subject':'subject_label'})

behav_data_fltr.head()

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  This is separate from the ipykernel package so we can avoid doing imports until
Out[63]:
subject_label ch_totanx ch_cdi_total
0 sub-010 51 11.0
1 sub-011 12 8.0
2 sub-013 8 5.0
3 sub-015 18 NaN
4 sub-024 21 1.0
In [64]:
# Add behavioral data to design matrix

subjs_info_kids_behav = subjs_info_kids_fltr.merge(behav_data_fltr, on='subject_label')


design_matrix = make_second_level_design_matrix(subjs_list_kids, subjs_info_kids_behav)

plot_design_matrix(design_matrix)

/Users/haroonpopal/anaconda3/envs/py37/lib/python3.7/site-packages/nilearn/glm/first_level/design_matrix.py:475: UserWarning: Attention: Design matrix is singular. Aberrant estimates are expected.
  warn('Attention: Design matrix is singular. Aberrant estimates '
Out[64]:
<AxesSubplot:label='conditions', ylabel='scan number'>
In [65]:
#design_matrix = make_second_level_design_matrix(subjs_list_kids, subjs_info_kids_fltr)
mni_mask = bids_dir+"/derivatives/fmriprep/sub-010/anat/sub-010_space-MNI152NLin2009cAsym_label-GM_probseg_bin.nii.gz"

#contrasts = ['valence_x_outcome',
#             'positive','positive_win','positive_loss',
#             'negative','negative_win','negative_loss']
contrasts=['all_winVlos', 'positive_winVlos']
contrasts_2nd = ['ch_totanx','ch_cdi_total']
tasks = ['mdoors','social']
group = 'kids'
alpha = 0.001


threshold_data = {}

for task in tasks:
    for contrast in contrasts:
        
        temp_file_list = []
        for subj in subjs_list_kids:
            temp_file = glob.glob(os.path.join(data_dir,subj,'zmap_'+task+'_'+contrast+'.nii'))
            temp_file_list.append(temp_file[0])
        temp_file_list.sort()
        
        print('Calculating '+task+' group '+contrast+' contrast')
        
        model = SecondLevelModel(mask_img=mni_mask, smoothing_fwhm=8.0)
        model.fit(temp_file_list, design_matrix=design_matrix)
        
        for contrast_2nd in contrasts_2nd:
            z_map = model.compute_contrast(contrast_2nd, output_type='z_score')

            z_map.to_filename(os.path.join(data_dir,'group',
                                       'zmap_'+group+'_'+task+'_'+contrast+'_'+contrast_2nd+'_unc.nii.gz'))
            
            # Multiple Comparisons Correction
            z_map_thresh, threshold = threshold_stats_img(z_map, alpha=alpha, height_control='fpr')
            z_map_thresh.to_filename(os.path.join(data_dir,'group',
                                       'zmap_'+group+'_'+task+'_'+contrast+'_'+contrast_2nd+'_fpr.nii.gz'))
            
            threshold_data['zmap_'+group+'_'+task+'_'+contrast+'_'+contrast_2nd+'_fpr_'+str(alpha)] = threshold

Calculating mdoors group all_winVlos contrast
Calculating mdoors group positive_winVlos contrast
Calculating social group all_winVlos contrast
Calculating social group positive_winVlos contrast
In [70]:
filename = 'zmap_kids_social_positive_winVlos_ch_cdi_total_'
threshold = threshold_data[filename+'fpr_'+str(alpha)]
#threshold=1

plot_stat_map_cb(filename+'unc', threshold)
In [69]:
filename = 'zmap_kids_social_all_winVlos_ch_cdi_total_'
threshold = threshold_data[filename+'fpr_'+str(alpha)]
#threshold=1

plot_stat_map_cb(filename+'unc', threshold)